def test_complex_stacking_xgboost(): # Ada over kFold over xgboost base_kfold = FoldingClassifier(base_estimator=XGBoostClassifier()) check_classifier(SklearnClassifier( clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)), has_staged_pp=False, has_importances=False)
def test_theanets_simple_stacking(): base_tnt = TheanetsClassifier() check_classifier(SklearnClassifier( clf=BaggingClassifier(base_estimator=base_tnt, n_estimators=3)), supports_weight=False, has_staged_pp=False, has_importances=False)
def test_complex_stacking_tmva(): # Ada over kFold over TMVA base_kfold = FoldingClassifier(base_estimator=TMVAClassifier(), random_state=13) check_classifier(SklearnClassifier( clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)), has_staged_pp=False, has_importances=False)
def test_simple_stacking_nolearn(): # AdaBoostClassifier fails because sample_weight is not supported in nolearn base_nl = NolearnClassifier() check_classifier(SklearnClassifier( clf=BaggingClassifier(base_estimator=base_nl, n_estimators=3)), has_staged_pp=False, has_importances=False, supports_weight=False)
def test_neurolab_stacking(): base_nlab = NeurolabClassifier(show=0, layers=[], epochs=N_EPOCHS2, trainf=nl.train.train_rprop) check_classifier(SklearnClassifier( clf=BaggingClassifier(base_estimator=base_nlab, n_estimators=3)), supports_weight=False, has_staged_pp=False, has_importances=False)
def test_simple_stacking_tmva(): base_tmva = TMVAClassifier() check_classifier(SklearnClassifier(clf=BaggingClassifier( base_estimator=base_tmva, n_estimators=3, random_state=13)), has_staged_pp=False, has_importances=False)
def test_simple_stacking_sklearn(): base_sk = AdaBoostClassifier(n_estimators=30) check_classifier( SklearnClassifier( clf=AdaBoostClassifier(base_estimator=base_sk, n_estimators=3)))
def test_simple_stacking_xgboost(): base_xgboost = XGBoostClassifier() classifier = SklearnClassifier( clf=AdaBoostClassifier(base_estimator=base_xgboost, n_estimators=3)) check_classifier(classifier, has_staged_pp=False)
def test_theanets_simple_stacking(): base_tnt = TheanetsClassifier(trainers=[{'min_improvement': 0.1}]) base_bagging = BaggingClassifier(base_estimator=base_tnt, n_estimators=3) check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)
def test_neurolab_stacking(): base_nlab = NeurolabClassifier(layers=[], epochs=N_EPOCHS2 * 2, trainf=nl.train.train_rprop) base_bagging = BaggingClassifier(base_estimator=base_nlab, n_estimators=3) check_classifier(SklearnClassifier(clf=base_bagging), **classifier_params)
def test_complex_stacking_tmva(): # Ada over kFold over TMVA base_kfold = FoldingClassifier(base_estimator=TMVAClassifier(factory_options="Silent=True:V=False:DrawProgressBar=False", method='kBDT', NTrees=10), random_state=13) check_classifier(SklearnClassifier(clf=AdaBoostClassifier(base_estimator=base_kfold, n_estimators=3)), has_staged_pp=False, has_importances=False)
def test_simple_stacking_tmva(): base_tmva = TMVAClassifier(factory_options="Silent=True:V=False:DrawProgressBar=False") check_classifier(SklearnClassifier(clf=BaggingClassifier(base_estimator=base_tmva, n_estimators=3, random_state=13)), has_staged_pp=False, has_importances=False)